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Adaptive Depth Estimation of Homography-Based Visual Servo with Finite-time Convergence

Adaptive Depth Estimation of Homography-Based Visual Servo w...

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data driven control and learning systems (ddcls)

作者： Qiwen Fang Xian Wang Yingbo Huangl Haoran He Faculty of Mechanical and Electrical Engineering Kunming University of Science and Technology Kunming China Yunnan Key Laboratory of Intelligent Control and Application Kunming University of Science and Technology Kunming China

ISBN: (数字)9798350361674

ISBN: (纸本)9798350361681

Although the homography-based visual servo (HBVS) has been studied for many decades, how to obtain the depth information with fast and accurate convergence performance has not been fully studied. In this paper, a finite-time (FT) adaptive depth estimation method is suggested for the manipulator regulation of the eye-in-hand (EIH) visual servo system. To realize this objective, a linearized camera model including depth information is first established. To obtain the depth, a set of auxiliary variables are constructed to extract the depth estimation error with a series of simple algebraic calculation. then, inspired by the sliding mode control, a FT adaptive depth estimation algorithm is developed, in which the predictor and/or observer error are not required, but the depth estimation error is employed to drive the estimation toward to its true value within finite time. With the estimated depth, the HBVS controller of the EIH system can regulate the robot arm to the desired position with fast response. Finally, comparative simulation results are given to show the efficiency of the proposed method.

关键词： Visualization Estimation error Adaptive systems Simulation Observers Manipulators Cameras

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SG-DLCT: Saliency-Guided Dual-Level Collaborative Transformer for Image Captioning

SG-DLCT: Saliency-Guided Dual-Level Collaborative Transforme...

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data driven control and learning systems (ddcls)

作者： Xinchao Zhu Jingzheng Deng Kanjian Zhang Jinxia Zhang The Key Laboratory of Measurement and Control of CSE Ministry of Education School of Automation Southeast University Nanjing China Southeast University Shenzhen Research Institute Shenzhen China

ISBN: (数字)9798350361674

ISBN: (纸本)9798350361681

Improving the generation of feature representations for images has been a central focus in the field of image captioning. However, existing approaches, whether based on grid features or region features, only consider the features within individual regions without capturing the interactions between objects. In this paper, we propose a novel Saliency-Guided Dual-Level Collaborative Transformer (SG-DLCT), which leverages saliency information to guide the network in capturing the importance of different objects and their interrelationships. Concretely, we utilize a pre-trained RGBD salient object ranking model to obtain saliency information. Building upon this, we propose the Saliency Feature Enhanced Module (SFEM), which optimizes region features using saliency values as weights, thereby guiding the model to focus on image regions that are more meaningful for generating image captions. Additionally, we propose the Saliency-Guided Cross Attention (SGCA), which leverages saliency information to model the interaction between region features and grid features, allowing the final model's features to benefit from the advantages of both types of features. Experimental results validate the significance of saliency information in image captioning tasks and demonstrate the effectiveness of our proposed method.

关键词： learning systems Buildings Collaboration Transformers control systems Task analysis

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Multi-Scale Boundary Perception Cascaded Segmentation Network for Pancreatic Lesione

Multi-Scale Boundary Perception Cascaded Segmentation Networ...

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data driven control and learning systems (ddcls)

作者： Jiashu Chen Qiubing Ren Yulin Yu Sheng Li Xiongxiong He College of Control Science and Engineering Zhejiang University Hangzhou P.R. China College of Information Engineering Zhejiang University of Technology Hangzhou Zhejiang P.R. China

ISBN: (数字)9798350361674

ISBN: (纸本)9798350361681

the segmentation of pancreatic lesions in CT images is crucial for clinical diagnosis and treatment. Nevertheless, accurately segmenting pancreatic lesions presents a challenge owing to the low contrast between the pancreas and surrounding tissues, the diverse anatomical structures of the pancreas and its lesions, and the inherent issue of blurred boundaries. this paper proposes a multi-scale boundary perception cascaded network (MBPC) for the segmentation of pancreatic lesions in CT images. To address the challenge of low contrast between the pancreas and surrounding tissues, we have developed an effective preprocessing scheme. the encoder features a multi-scale fusion module, facilitating the extraction of semantic information at different scales and mitigating the impact of shape transformations. then, We introduced the Boundary-Aware Module (BAM) and the Boundary Fusion Module (BFM) to extract boundary information for the pancreas and its lesions, thereby improving the model's performance in addressing edge-related tasks within the pancreatic and lesion regions. Finally, the proposed method is experimentally validated on public datasets, demonstrating competitive performance compared to some state-of-the-art pancreatic segmentation algorithms.

关键词： learning systems Image segmentation Shape Computed tomography Semantics Feature extraction Lesions

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Model Reconstruction Based on Physical-Informed Polynomial Neural ODEs

Model Reconstruction Based on Physical-Informed Polynomial N...

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data driven control and learning systems (ddcls)

作者： Zhang Chen Wei Zhu Jingsui Li Hanlin Bian Chao Pei School of Computer Science and Technology Chongqing University of Posts and Telecommunications Chongqing P. R. China Key Laboratory of Intelligent Analysis and Decision on Complex Systems Chongqing University of Posts and Telecommunications Chongqing P. R. China

ISBN: (数字)9798350361674

ISBN: (纸本)9798350361681

Neural networks are capable function approximators for data, but their lack of interpretability poses challenges. Physical-informed Polynomial Neural Ordinary Differential Equations (PIPNODEs) are proposed. they combine the constraints of prior knowledge of the data with interpretable Polynomial Neural Ordinary Differential Equations (PolyNODEs) to discover or approximate the governing equations. In this work, Neural Ordinary Differential Equations (NODEs) are categorized into two layers: a physical-informed term, which represents the priori knowledge of the data, and a discrepancy term, which is derived from the approximation of PolyNODEs. Pruning strategies are also used to remove redundant terms with coefficients lower than a certain threshold. We demonstrate the effectiveness and flexibility of this framework in a series of numerical algorithms with varying levels of prior knowledge. We found that having correct but incomplete physical information accelerates training and approaching thresholds compared to having incorrect physical information.

关键词： Training Accuracy Heuristic algorithms Ordinary differential equations Approximation algorithms Mathematical models data models

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Adaptive Super-Twisting Sliding Mode control for Friction Compensation of Servo System Via AESO and Parameters Estimation

Adaptive Super-Twisting Sliding Mode Control for Friction Co...

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data driven control and learning systems (ddcls)

作者： Shun Wang Liang Tao Xiongfeng Deng Binzi Xu Linlin Shi College of Electrical Engineering Anhui Polytechnic University Wuhu China College of Engineering South China Agricultural University Guangzhou China

ISBN: (数字)9798350361674

ISBN: (纸本)9798350361681

this paper presents a super-twisting sliding mode control strategy utilizing an adaptive extended state observer (AESO) for servo systems with nonlinear friction compensation to tackle the challenges of precise tracking. Firstly, an advanced online parameter identification mechanism is adopted to identify the unknown parameters of the continuous friction model within the system. this mechanism dynamically updates parameters in real-time, effectively converging to the true values of the parameters in response to the dynamic changes of the system. Subsequently, the AESO is employed to accurately estimate and compensate for the lumped disturbances, encompassing external disturbances and parameter estimation errors. then, an adaptive super-twisting sliding mode controller is proposed. the controller is meticulously engineered to ensure the system's state accurately adheres to a predefined reference trajectory, effectively reducing errors stemming from model uncertainties and the influence of external disturbances. To empirically substantiate the efficacy of the proposed methods, a comprehensive series of simulation experiments are conducted, showcasing the superiority of the proposed method in terms of tracking accuracy and system stability.

关键词： Adaptation models Parameter estimation Uncertainty Adaptive systems Friction Dynamics Observers

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Reinforcement learning with Neighborhood Search and Self-learned Rules

Reinforcement Learning with Neighborhood Search and Self-lea...

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data driven control and learning systems (ddcls)

作者： Xiao Yin Yuanyang Zhu Chunlin Chen Department of Control Science and Intelligent Engineering Nanjing University Nanjing China

ISBN: (数字)9798350361674

ISBN: (纸本)9798350361681

Reinforcement learning (RL), induced by the exploration-exploitation dilemma, usually requires enough interaction experiences with the environment to improve performance. Rule-based methods utilize the internal expertise of tasks to benefit the learning process of the target task and can be shared with similar goal-setting tasks, which is also one form of knowledge transfer with better interpretability. However, symbolic rules or prior knowledge of the task may not be obtained in advance for most control tasks. In this paper, we propose an effective approach named neighborhood search and self-learned rules (RL-NSSR), where the neighborhood search library and the rules can be learned from the samples of a set of collected trajectories. In the RL- NSSR, first, the expert policy is obtained by training the RL agent in the discretized continuous state-action space task for collecting the trajectories data. Second, we extract the rules using the collected data via ensemble decision tree methods, and select the states of the trajectories with higher cumulative rewards as the neighborhood states to speed up the learning process. third, we employ both the neighborhood search and the self-learned symbolic rules in a fine-grained way and a rough way, respectively, to guide the exploration strategy for accelerating the learning process. Experiments conducted on several groups of locomotion tasks by the MuJoCo physics engine demonstrate that the proposed RL-NSSR approach can achieve improved control performances for both simple and complex tasks.

关键词： Training learning systems Reinforcement learning Libraries Trajectory Decision trees Task analysis

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data Augmentation Integrated with Feature-Enhanced Convolutional Neural Network for Imbalanced Fault Diagnosis in Rolling Bearings

Data Augmentation Integrated with Feature-Enhanced Convoluti...

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data driven control and learning systems (ddcls)

作者： Li Wang Qilin Qu Yanwei Wang David Shan-Hill Wong Ying Zheng China -Belt and Road Joint Laboratory On Measurement and Control Technology School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan China School of Mechanical & Electrical Engineering Wuhan Institute of Technology Wuhan Department of Chemical Engineering National Tsing-Hua University Hsinchu Taiwan

ISBN: (数字)9798350361674

ISBN: (纸本)9798350361681

Imbalance in fault samples is a key problem with most rolling bearing datasets in real industrial processes, limiting the performance of fault detection. In addition, existing fault diagnosis methods for rolling bearings do not adopt a modular division between the initial layer and the classification layer. this lack of modularization inhibits the full activation of the network's feature extraction capabilities and impedes subsequent model improvements. In this paper, a novel fault diagnosis method based on data augmentation integrated with a feature-enhanced convolutional neural network (DA-FECNN) is proposed. First, Variational Autoencoder-Generative Adversarial Network (VAE-GAN) is utilized to expand the number of fault samples and improve the diversity of few-shot. Second, a uniform feature-enhanced structure is designed with a feature extraction module, a multi-scale classification module, and a reconstruction module. the network's ability on feature extraction is improved by focusing on high quality features. thereby, accurate fault classification is realized simultaneously. Experiments on real world data show the effectiveness of the proposed method on fault diagnosis task.

关键词： Fault diagnosis learning systems Accuracy Limiting Rolling bearings Feature extraction data augmentation

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DA-TCN Based Temperature Prediction Algorithm for Heating Furnace

DA-TCN Based Temperature Prediction Algorithm for Heating Fu...

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data driven control and learning systems (ddcls)

作者： Yidan Xin Shaolin Hu Guotao Yang Xi'an University of Technology Xi'an China Guangdong University of Petrochemical Technology Maoming China

ISBN: (数字)9798350361674

ISBN: (纸本)9798350361681

During the operation of the heating furnace, accurate perception and advance prediction of the furnace temperature are of great significance for preventing safety hazards in the petrochemical process and ensuring the safe operation of the heating furnace. Traditional prediction models have low accuracy in predicting actual working condition data. this paper proposes a Dual-Stage Attention-Based Temporal Convolutional Network (short as DA-TCN) prediction model based on the dual-stage attention mechanism (short as DA). the input variable attention mechanism and time attention mechanism are used to weight sequence features, and then the furnace temperature of the heating furnace is predicted based on the temporal convolutional neural network. the effectiveness of the proposed method is validated based on the sampled data. Measured data are used to validate the effectiveness of this method. the prediction results t of the DA-TCN model, CNN-LSTM model, and DA-LSTM model are compared, which shows that the proposed prediction model can effectively improve the prediction accuracy. Compared with the latter two, the root mean square error of the DA-TCN model decreased by 55% and 89%, and the mean absolute error decreased by 74% and 85%, respectively, which further demonstrates the feasibility and effectiveness of the proposed method.

关键词： Heating systems Temperature measurement Accuracy Attention mechanisms Furnaces Neural networks Predictive models

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Establishment and Analysis of Passive Localization Model for a Class of Unmanned Aerial Vehicle systems

Establishment and Analysis of Passive Localization Model for...

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data driven control and learning systems (ddcls)

作者： Liwen Yu Qianru Yang Yuqian Li Ruikun Zhang School of Mathematics and Physics Qingdao University of Science and Technology Qingdao China

ISBN: (数字)9798350361674

ISBN: (纸本)9798350361681

In order to avoid the interference of external magnetic field, UAV swarms should keep electromagnetic silence while flying, and using pure azimuth passive localization to adjust the position of UAVs is an effective method. the pure azimuth passive localization problem for UAVs includes two scenarios: known and unknown positions and quantities of UAVs emitting signals. For the case that the number and position of the three UAVs transmitting signals are known, a localization model based on the sine-cosine theorem, iteration and other methods for passively receiving signal UAVs is established for adjusting the position of the UAVs in order to realize the effective adjustment of the position of the UAVs. For the case that the number and quantity of some UAVs transmitting signals are unknown, this paper proposes an effective localization model of UAVs with a slight deviation of a certain position based on the polar coordinate localization method and the characteristics of the hidden circle model. Meanwhile, the relevant properties of the sine-cosine function are utilized to determine the uniqueness of the solution. Finally, the accuracy of the model is verified by numerical simulation. the results show that the theoretical credibility of the method proposed in this paper is strong and provides a more reliable theoretical support for the research in the field of UAV technology.

关键词： Location awareness Machine learning algorithms Accuracy Azimuth Reliability theory Sensor fusion Autonomous aerial vehicles

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Sequential POI Recommendation Based on Graph Neural Networks

Sequential POI Recommendation Based on Graph Neural Networks

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data driven control and learning systems (ddcls)

作者： Bo Shao Zhichun Jia Hongda Wang Yiwen Wang Xiyu Zhang Xing Xing College of Information Science and Technology Bohai University

ISBN: (数字)9798350361674

ISBN: (纸本)9798350361681

With the rapid rise of location-based social networks, POI (point-of-interest) recommendation has become a hotspot in the field of recommendation research; however, existing methods ignore user interactions and POI transfer choices, and the user's preference keeps changing over time, leading to a decrease in the accuracy of recommendation. In order to solve the problem of dynamic changes in user preferences over time, we propose a model based on graph neural networks. the model firstly utilizes the gating mechanism to learn the features of users containing interpersonal connections, and then performs feature extraction; secondly, after demonstrating the intricate relationship between visitors and arrivals using the graph attention layer. We then apply the multi-head attention mechanism based on this so that potential feature vectors (embeddings) focus on multifaceted information, thus making their associations more accurate. the user's preferences are then captured and finally the next POI is recommended. Experiment using two real datasets, Gowalla and Foursquare, show that our proposed approach improves recall and mrr by 5% and 3%, respectively, as compared to other approaches, thus suggesting that this improvement has better recommendation performance.

关键词： learning systems Adaptation models Technological innovation Accuracy Social networking (online) Feature extraction Graph neural networks

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